Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief

Christian Muise, Vaishak Belle, Paolo Felli, Sheila McIlraith, Tim Miller, Adrian R. Pearce, Liz Sonenberg

Research output: Contribution to journalArticlepeer-review


Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.
Original languageEnglish
Article number103605
Number of pages36
JournalArtificial Intelligence
Early online date7 Oct 2021
Publication statusPublished - 1 Jan 2022


  • automated planning
  • epistemic planning
  • knowledge and belief


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